1 Load Libraries

library(knitr)
library(readxl)
library(openxlsx)
library(dplyr)
library(tidyr)
library(DescTools)
library(DT)
library(ggplot2)
library(ggiraph)
library(gridExtra)
library(plotly)

LibrariesVersions <- data.frame(
  Library = c("knitr", "readxl", "openxlsx", "dplyr", "tidyr", 
              "DescTools", "DT", "ggplot2", "ggiraph", "gridExtra", "plotly"), 
  Version = c(as.character(packageVersion("knitr")), as.character(packageVersion("readxl")), 
              as.character(packageVersion("openxlsx")), as.character(packageVersion("dplyr")), 
              as.character(packageVersion("tidyr")), as.character(packageVersion("DescTools")), 
              as.character(packageVersion("DT")), as.character(packageVersion("ggplot2")), 
              as.character(packageVersion("ggiraph")), as.character(packageVersion("gridExtra")), 
              as.character(packageVersion("plotly"))))

datatable(LibrariesVersions, options = list(scrollX = TRUE, autoWidth = TRUE), 
          caption = paste("Libraries Versions | R",  
                          gsub("^R version\\s*|\\s*\\(\\d{4}-\\d{2}-\\d{2} ucrt\\)", "", R.version.string)),
          style = "bootstrap", rownames = FALSE) %>%
          formatStyle(columns = 1, textAlign = "left") %>% formatStyle(columns = 2, textAlign = "center")

2 ISO 3166-1 and M49 Codes

ISO 3166-1 is an international standard from International Organization for Standardization (ISO), with 3 codes:

Alpha-2 Code (ISO2)
Alpha-3 Code (ISO3)
Numeric Code

Numeric Code \(\equiv\) M49 Code from United Nations Statistic Division (UNSD). ISO 3166-1 is available for autonomous countries or territories, while M49 is also available for world or continents.

How to obtain a code? Be a United Nations (UN) or a UN Specialized Agencies (FAO | World Bank | WHO | …) Member State.

There are more Numeric/M49 Code (249) than countries in UN (198).

3 International Migrant Stock Data

Data are taken from UN | Population Division. Estimates of the number (or “stock”) of international migrants (1990-2020) are available for 235 countries/areas of the world. The international migrant population corresponds to the total number of migrants present in a given country at a given moment in time (foreign-born). Tourists are not counted among migrants.

Data are organized on the basis of Residence-Birth pairs according to certain classifications or countries/areas:

6 Continental Regions with 22 Sub-Regions and Intermediate Regions:
- Africa
- Asia
- Europe
- Latin America and the Caribbean
- Northern America
- Oceania
8 Geographic Regions based on the Sustainable Development Goals (SDG) Classification from UN:
- Australia and New Zealand
- Central and Southern Asia
- Eastern and South-Eastern Asia
- Europe and Northern America
- Latin America and the Caribbean
- Northern Africa and Western Asia
- Oceania (Australia and New Zealand excluded)
- Sub-Saharan Africa
2 Development Levels:
- More Developed Regions:
  - Australia and New Zealand and Japan
  - Europe
  - Northern America
- Less Developed Regions:
  - Africa
  - Asia (Japan excluded)
  - Latin America and the Caribbean
  - Oceania (Australia and New Zealand excluded)
3 Development Levels (137/232) from OHRLLS:
- Least Developed Countries (LDC | 47)
- Land-Locked Developing Countries (LLDC | 32)
- Small Island Developing States (SIDS | 58)
3/4 Income Levels based on Gross National Income (GNI | World Bank 2020):
- High-Income Countries
- Middle-Income Countries
  - Upper-Middle-Income Countries
  - Lower-Middle-Income Countries
- Low-Income Countries

Remark: Income Levels are not available for all countries/areas.

3.1 Data Exploration

International Migrant Stocks in the World (Residence) born in Europe and Northern America (Birth) are shown in this table. We have estimates from 1990 to 2020 (Year).

#Load XLSX File
iMIGRANT_Stock_Total <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>%
  select(2, 4, 6:14) %>%
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
#One line (with all Years) for each Residence-Birth to one line/Year for each Residence-Birth
iMIGRANT_Stock_Total <- pivot_longer(iMIGRANT_Stock_Total, cols = 5:11) %>% rename(Year = 5, iMIGRANT_Stock_Total = 6)

#iMIGRANT_Stock_Males
iMIGRANT_Stock_Males <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>% 
  select(2, 4, 6:7, 15:21) %>% 
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
iMIGRANT_Stock_Males <- pivot_longer(iMIGRANT_Stock_Males, cols = 5:11) %>% rename(Year = 5, iMIGRANT_Stock_Males = 6)

#iMIGRANT_Stock_Females
iMIGRANT_Stock_Females <- read_xlsx(
  "../Données/IMS_from_ONU/ims_sex_residence_and_birth_2020.xlsx", sheet = "Table 1", skip = 8) %>% 
  select(2, 4, 6:7, 22:28) %>% 
  rename(Residence = 1, ResidenceCode = 2, Birth = 3, BirthCode = 4, 
         `1990` = 5, `1995` = 6, `2000` = 7, `2005` = 8, `2010` = 9, `2015` = 10, `2020` = 11)
iMIGRANT_Stock_Females <- pivot_longer(iMIGRANT_Stock_Females, cols = 5:11) %>% rename(Year = 5, iMIGRANT_Stock_Females = 6)

#Combine iMIGRANT_Stock_Total | iMIGRANT_Stock_Males | iMIGRANT_Stock_Females
iMIGRANT_Stock <- cbind(
  iMIGRANT_Stock_Total, iMIGRANT_Stock_Males[length(iMIGRANT_Stock_Males)], iMIGRANT_Stock_Females[length(iMIGRANT_Stock_Females)])

#Example: World-(Europe and NA)
kable(iMIGRANT_Stock[57:63, c(1, 3, 5:8)], row.names = FALSE, align = c("l", "l", "c", "c", "c", "c"),
      caption = "World's Migrant Stocks born in Europe and Northern America (1990-2020)")

World’s Migrant Stocks born in Europe and Northern America (1990-2020)
Residence	Birth	Year	iMIGRANT_Stock_Total	iMIGRANT_Stock_Males	iMIGRANT_Stock_Females
WORLD	Europe and Northern America	1990	50532413	23704534	26827879
WORLD	Europe and Northern America	1995	51582164	24148758	27433406
WORLD	Europe and Northern America	2000	52877703	24676104	28201599
WORLD	Europe and Northern America	2005	54849090	25682798	29166292
WORLD	Europe and Northern America	2010	58681104	27337252	31343852
WORLD	Europe and Northern America	2015	60592402	28229706	32362696
WORLD	Europe and Northern America	2020	67601621	31770385	35831236

This was an example between World-(Europe and Northern America). Data are also available for more areas/countries (282).

#Residence(s) in XLSX
Residences_iMIGRANT_Stock <- unique(iMIGRANT_Stock[c("Residence", "ResidenceCode")])
datatable(Residences_iMIGRANT_Stock, options = list(scrollX = TRUE, autoWidth = TRUE), 
          caption = "Residences in International Migrant Stock Data", style = "bootstrap", rownames = FALSE) %>%
  # formatStyle(columns = 1, textAlign = "left", `text-transform` = "uppercase") %>%
  formatStyle(columns = 1, textAlign = "left") %>% formatStyle(columns = 2, textAlign = "center")

A few minor alterations have been made to Residence and Birth columns.

#Columns: Residence | Birth
iMIGRANT_Stock.DATA <- iMIGRANT_Stock %>%
  mutate_if(is.character, ~case_when(
    . == "Oceania (excluding Australia and New Zealand)" ~ "Oceania (Australia and New Zealand excluded)",
    . == "Developed regions" ~ "More Developed Countries",
    . == "Less developed regions" ~ "Less Developed Countries",
    . == "Less developed regions, excluding least developed countries" ~ 
      "Less Developed Countries (Least Developed Countries excluded)",
    . == "Less developed regions, excluding China" ~ "Less Developed Countries (China excluded)",
    . == "Least developed countries" ~ "Least Developed Countries (LDC)",
    . == "Land-locked Developing Countries (LLDC)" ~ "Land-Locked Developing Countries (LLDC)",
    . == "Small island developing States (SIDS)" ~ "Small Island Developing States (SIDS)",
    . == "High-income countries" ~ "High-Income Countries",
    . == "Middle-income countries" ~ "Middle-Income Countries",
    . == "Upper-middle-income countries" ~ "Upper-Middle-Income Countries",
    . == "Lower-middle-income countries" ~ "Lower-Middle-Income Countries",
    . == "Low-income countries" ~ "Low-Income Countries",
    . == "Bolivia (Plurinational State of)" ~ "Bolivia",
    . == "China, Hong Kong SAR*" ~ "Hong Kong SAR",
    . == "China, Macao SAR*" ~ "Macao SAR",
    . == "China, Taiwan Province of China*" ~ "Taiwan (Province of China)",
    . == "Iran (Islamic Republic of)" ~ "Iran",
    . == "Micronesia (Fed. States of)" ~ "Micronesia",
    . == "Saint Helena*" ~ "Saint Helena, Ascension and Tristan da Cunha",
    . == "Saint Martin (French part)*" ~ "Saint Martin",
    . == "Sint Maarten (Dutch part)*" ~ "Sint Maarten",
    . == "Turkey" ~ "Türkiye",
    . == "United Kingdom*" ~ "United Kingdom of Great Britain and Northern Ireland",
    . == "Venezuela (Bolivarian Republic of)" ~ "Venezuela", TRUE ~ .)) %>%
  mutate(across(c(Residence, Birth), ~gsub("\\*", "", .)))

3.2 Visualization (Continental Regions)

Remark: The Stocks constitute a fraction of the information, we need to know the Shares.

We need to sum the International Migrant Stocks from these 6 Regions to calculate the Shares.

#Keep iMIGRANT_Stock from CRs (6)
iMIGRANT_Stock.FDATA <- iMIGRANT_Stock.DATA %>% filter(Residence == "WORLD", BirthCode %in% c(903, 935, 908, 904, 905, 909))

#Make ∑/Year
iMIGRANT_Stock.FDATA_CRs <- iMIGRANT_Stock.FDATA %>% group_by(Year) %>% 
  summarize(iMIGRANT_Stock_Total_CRs = sum(iMIGRANT_Stock_Total), 
            iMIGRANT_Stock_Males_CRs = sum(iMIGRANT_Stock_Males), 
            iMIGRANT_Stock_Females_CRs = sum(iMIGRANT_Stock_Females))

#Check Correct ∑
iMIGRANT_Stock.DATA_World <- iMIGRANT_Stock.DATA %>% filter(Residence == "WORLD", Birth == "WORLD")

#∑/Year !Correct => Omitted DATA
iMIGRANT_Stock.FDATA_CRs <- merge(iMIGRANT_Stock.FDATA_CRs, iMIGRANT_Stock.DATA_World, by = "Year") %>% 
  rename(iMIGRANT_Stock_Total_World = iMIGRANT_Stock_Total, 
         iMIGRANT_Stock_Males_World = iMIGRANT_Stock_Males, 
         iMIGRANT_Stock_Females_World = iMIGRANT_Stock_Females) %>%
  mutate(
    Share_of_Omitted_Stock_Total = round(((iMIGRANT_Stock_Total_World - iMIGRANT_Stock_Total_CRs) / iMIGRANT_Stock_Total_World) * 100, 3),
    Share_of_Omitted_Stock_Males = round(((iMIGRANT_Stock_Males_World - iMIGRANT_Stock_Males_CRs) / iMIGRANT_Stock_Males_World) * 100, 3),
    Share_of_Omitted_Stock_Females = 
      round(((iMIGRANT_Stock_Females_World - iMIGRANT_Stock_Females_CRs) / iMIGRANT_Stock_Females_World) * 100, 3)) %>% 
  select(5:8, 1, 9, 2, 12, 10, 3, 13, 11, 4, 14)

#Table World-World
datatable(iMIGRANT_Stock.FDATA_CRs[, c(1, 3, 5:14)], options = list(scrollX = TRUE, autoWidth = TRUE), 
          style = "bootstrap", rownames = FALSE, caption = "World's Migrant Stocks born in World (1990-2020)") %>%
  formatStyle(columns = c(1, 2), textAlign = "left") %>% formatStyle(columns = c(3:14), textAlign = "center")

iMIGRANT_Stock_Total_CRs \(<\) World-World Migrant Stocks (iMIGRANT_Stock_Total_World). There is 4-5.5% omitted data (Share_of_Omitted_Stock_Total) in iMIGRANT_Stock_Total_CRs. Where are these 4-5.5%? XLSX file include a row with migrants born in Other for countries but not for Regions, so we sum all these Other rows (iMIGRANT_Stock_Total_OTHER). iMIGRANT_Stock_Total_OTHER match Share_of_Omitted_Stock_Total?

#Keep Other/Countries
iMIGRANT_Stock.DATA_OTHERs <- iMIGRANT_Stock.DATA %>% filter(Birth == "Other")

#∑/Year
iMIGRANT_Stock.DATA_OTHER <- iMIGRANT_Stock.DATA_OTHERs %>% group_by(Year) %>%
  summarize(iMIGRANT_Stock_Total_OTHER = sum(iMIGRANT_Stock_Total), 
            iMIGRANT_Stock_Males_OTHER = sum(iMIGRANT_Stock_Males), 
            iMIGRANT_Stock_Females_OTHER = sum(iMIGRANT_Stock_Females))

#Variable(s)...
iMIGRANT_Stock.DATA_OTHER <- iMIGRANT_Stock.DATA_OTHER %>% 
  mutate(
    Residence = iMIGRANT_Stock.FDATA_CRs$Residence, 
    ResidenceCode = iMIGRANT_Stock.FDATA_CRs$ResidenceCode,
    Birth = "OTHER", BirthCode = 2003) %>% select(5:8, everything())

#Table World-OTHER
kable(iMIGRANT_Stock.DATA_OTHER[ , c(1, 3, 5:8)], row.names = FALSE, 
      align = c("l", "l", "c", "c", "c", "c"), 
      caption = "World's Migrant Stocks born in Other (1990-2020)")

World’s Migrant Stocks born in Other (1990-2020)
Residence	Birth	Year	iMIGRANT_Stock_Total_OTHER	iMIGRANT_Stock_Males_OTHER	iMIGRANT_Stock_Females_OTHER
WORLD	OTHER	1990	8640334	4408990	4231344
WORLD	OTHER	1995	7477497	3776224	3701273
WORLD	OTHER	2000	7063730	3593958	3469772
WORLD	OTHER	2005	7820665	4096864	3723801
WORLD	OTHER	2010	8757236	4601492	4155744
WORLD	OTHER	2015	10918842	5685512	5233330
WORLD	OTHER	2020	12657151	6533198	6123953

iMIGRANT_Stock_Total_World \(=\) iMIGRANT_Stock_Total_CRs \(+\) iMIGRANT_Stock_Total_OTHER. We can include a seventh region called OTHER to make our visualization. Now, we are able to calculate the shares for all regions (Share_of_iMIGRANT_Stock_Total).

#DATA MANIPULATION...
iMIGRANT_Stock.DATA_OTHER <- iMIGRANT_Stock.DATA_OTHER %>% 
  rename(iMIGRANT_Stock_Total = iMIGRANT_Stock_Total_OTHER, 
         iMIGRANT_Stock_Males = iMIGRANT_Stock_Males_OTHER, 
         iMIGRANT_Stock_Females = iMIGRANT_Stock_Females_OTHER)
iMIGRANT_Stock.FDATA <- rbind(iMIGRANT_Stock.FDATA, iMIGRANT_Stock.DATA_OTHER)

#Calculate SHARE/iMIGRANT_Stock_Total
SHAREs <- list()
for (ROW in 1:nrow(iMIGRANT_Stock.FDATA)) {
  YYYY <- iMIGRANT_Stock.FDATA$Year[ROW]
  iMIGRANT_Stock <- iMIGRANT_Stock.FDATA_CRs$iMIGRANT_Stock_Total_World[iMIGRANT_Stock.FDATA_CRs$Year == YYYY]
  SHARE <- round((iMIGRANT_Stock.FDATA$iMIGRANT_Stock_Total[ROW] / iMIGRANT_Stock) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
iMIGRANT_Stock.FDATA$Share_of_iMIGRANT_Stock_Total <- unlist(SHAREs)

#Calculate SHARE/iMIGRANT_Stock_Males
SHAREs <- list()
for (ROW in 1:nrow(iMIGRANT_Stock.FDATA)) {
  YYYY <- iMIGRANT_Stock.FDATA$Year[ROW]
  iMIGRANT_Stock <- iMIGRANT_Stock.FDATA_CRs$iMIGRANT_Stock_Males_World[iMIGRANT_Stock.FDATA_CRs$Year == YYYY]
  SHARE <- round((iMIGRANT_Stock.FDATA$iMIGRANT_Stock_Males[ROW] / iMIGRANT_Stock) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
iMIGRANT_Stock.FDATA$Share_of_iMIGRANT_Stock_Males <- unlist(SHAREs)

#Calculate SHARE/iMIGRANT_Stock_Females
SHAREs <- list()
for (ROW in 1:nrow(iMIGRANT_Stock.FDATA)) {
  YYYY <- iMIGRANT_Stock.FDATA$Year[ROW]
  iMIGRANT_Stock <- iMIGRANT_Stock.FDATA_CRs$iMIGRANT_Stock_Females_World[iMIGRANT_Stock.FDATA_CRs$Year == YYYY]
  SHARE <- round((iMIGRANT_Stock.FDATA$iMIGRANT_Stock_Females[ROW] / iMIGRANT_Stock) * 100, 5)
  SHAREs[[ROW]] <- SHARE}
iMIGRANT_Stock.FDATA$Share_of_iMIGRANT_Stock_Females <- unlist(SHAREs)

#Table World-(Europe and NA)
datatable(iMIGRANT_Stock.FDATA[15:21, c(1, 3, 5:11)], options = list(scrollX = TRUE, autoWidth = TRUE), 
          style = "bootstrap", rownames = FALSE, caption = "World's Migrant Stocks (%) born in EUROPE (1990-2020)") %>%
  formatStyle(columns = c(1, 2), textAlign = "left") %>% 
  formatStyle(columns = c(3:9), textAlign = "center") %>% formatRound(columns = c(7:9), digits = 3)

Share_of_iMIGRANT_Stock_Total in World born in EUROPE is down, from 31.20% in 1990 to 22.55% in 2020.

To visualize International Migrant Stock Data (Stocks and Shares), we will use 4 libraries (ggplot2 | ggplotly | ggiraph | plotly).

3.2.1 Evolution

We will use Line Plots and Stacked Barplots to illustrate International Migrant Stock Evolution (in Millions) from 1990 to 2020. Stacked Barplots will show International Migrant Stock Evolution in a Residence Area from \(n\) Birth Areas.

3.2.1.1 Line Plots

#N°1
LINEs <- ggplot(iMIGRANT_Stock.FDATA_CRs, aes(x = Year)) +
  geom_line(aes(y = iMIGRANT_Stock_Total_World, color = "iMIGRANT_Stock_Total_World", group = "iMIGRANT_Stock_Total_World")) +
  geom_line(aes(y = iMIGRANT_Stock_Males_World, color = "iMIGRANT_Stock_Males_World", group = "iMIGRANT_Stock_Males_World")) +
  geom_line(aes(y = iMIGRANT_Stock_Females_World, color = "iMIGRANT_Stock_Females_World", group = "iMIGRANT_Stock_Females_World")) +
  geom_point(aes(y = iMIGRANT_Stock_Total_World, color = "iMIGRANT_Stock_Total_World", group = "iMIGRANT_Stock_Total_World")) +
  geom_point(aes(y = iMIGRANT_Stock_Males_World, color = "iMIGRANT_Stock_Males_World", group = "iMIGRANT_Stock_Males_World")) +
  geom_point(aes(y = iMIGRANT_Stock_Females_World, color = "iMIGRANT_Stock_Females_World", group = "iMIGRANT_Stock_Females_World")) +
  labs(title = "International MIGRANT Stocks in World (1990-2020)", y = "International MIGRANT Stock") +
  #200 000 To 200M
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  scale_color_manual(
    values = c("iMIGRANT_Stock_Total_World" = "black", "iMIGRANT_Stock_Males_World" = "blue", "iMIGRANT_Stock_Females_World" = "pink"), 
    labels = c("iMIGRANT_Stock_Total_World" = "Both Sexes Combined", 
               "iMIGRANT_Stock_Males_World" = "Males", "iMIGRANT_Stock_Females_World" = "Females")) +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "top", legend.title = element_blank())
LINEs

We can follow International Migrant Stock Evolution, which has become more intense since 2005. Stocks are balanced in a fair manner between males and females (1990-2005), then leans in favor of males (2005-2020).

We don’t have accurate Stocks because it’s static, we need to make it interactive. We also have to rescale Stocks to add “M” suffix on y-axis.

#N°2
LINEs_INTERACTIVE_One <- plot_ly(data = iMIGRANT_Stock.FDATA_CRs, x = ~Year, y = ~iMIGRANT_Stock_Total_World, 
                                 type = 'scatter', name = 'Both Sexes Combined', 
                                 mode = 'lines+markers', line = list(color = 'black'), marker = list(color = 'black'), 
                                 text = with(iMIGRANT_Stock.FDATA_CRs, paste(" Residence:", Residence, "<br>",
                                                                             "Birth:", Birth, "<br>", 
                                                                             "Year:", Year, "<br>",
                                                                             "International MIGRANT Stock (Both Sexes Combined):", 
                                                                             paste0(format(x = iMIGRANT_Stock_Total_World, trim = TRUE, 
                                                                                           scientific = FALSE, big.mark = " "), "M"))), 
                                 hoverinfo = "text") %>%
  add_trace(x = ~Year, y = ~iMIGRANT_Stock_Males_World, name = 'Males', line = list(color = 'blue'), marker = list(color = 'blue'), 
            text = with(iMIGRANT_Stock.FDATA_CRs, paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                        "International MIGRANT Stock (Males):", 
                                                        paste0(format(x = iMIGRANT_Stock_Males_World, trim = TRUE, scientific = FALSE, 
                                                                      big.mark = " "), "M"))), hoverinfo = "text") %>%
  add_trace(x = ~Year, y = ~iMIGRANT_Stock_Females_World, name = 'Females', line = list(color = 'pink'), marker = list(color = 'pink'), 
            text = with(iMIGRANT_Stock.FDATA_CRs, paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                        "International MIGRANT Stock (Females):", 
                                                        paste0(format(x = iMIGRANT_Stock_Females_World, trim = TRUE, scientific = FALSE, 
                                                                      big.mark = " "), "M"))), hoverinfo = "text") %>% 
  layout(title = "International MIGRANT Stocks in World (1990-2020)", 
         xaxis = list(title = "Year"), yaxis = list(title = "International MIGRANT Stock"),
         legend = list(x = 0.5, y = 1.05, xanchor = "center", orientation = "h"))
LINEs_INTERACTIVE_One

A download button is available. We can see Residence | Birth | Year | Stocks in Hover Text. We don’t need to rescale Stocks to add “M” suffix on y-axis, we need to do it for Hover Text.

#N°3
LINEs_INTERACTIVE_Two <- ggplotly(LINEs) %>% 
  layout(legend = list(title = "", x = 0.5, y = 1.05, xanchor = "center", orientation = "h"))
for (i in 1:length(LINEs_INTERACTIVE_Two$x$data)) {
  LINEs_INTERACTIVE_Two$x$data[[i]]$name <- c("Both Sexes Combined", "Males", "Females")[i]}
LINEs_INTERACTIVE_Two

We can’t remove default Hover Text and text argument within aes() in geom_point() does not allow for modifications to Hover Text. Customs labels have to be redone here.

#N°4
LINEs_INTERACTIVE_THREE <- ggplot(iMIGRANT_Stock.FDATA_CRs) +
  geom_line_interactive(aes(x = Year, y = iMIGRANT_Stock_Total_World, color = "iMIGRANT_Stock_Total_World", group = "iMIGRANT_Stock_Total_World")) +
  geom_line_interactive(aes(x = Year, y = iMIGRANT_Stock_Males_World, color = "iMIGRANT_Stock_Males_World", group = "iMIGRANT_Stock_Males_World")) +
  geom_line_interactive(aes(
    x = Year, y = iMIGRANT_Stock_Females_World, color = "iMIGRANT_Stock_Females_World", group = "iMIGRANT_Stock_Females_World")) +
  geom_point_interactive(aes(x = Year, y = iMIGRANT_Stock_Total_World, color = "iMIGRANT_Stock_Total_World", group = "iMIGRANT_Stock_Total_World", 
                             tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                             "<br>International MIGRANT Stock (Both Sexes Combined):", 
                                             paste0(format(x = iMIGRANT_Stock_Total_World, trim = TRUE, 
                                                           scientific = FALSE, big.mark = " "), "M")))) +
  geom_point_interactive(aes(x = Year, y = iMIGRANT_Stock_Males_World, color = "iMIGRANT_Stock_Males_World", group = "iMIGRANT_Stock_Males_World", 
                             tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year,
                                             "<br>International MIGRANT Stock (Males):", 
                                             paste0(format(x = iMIGRANT_Stock_Males_World, trim = TRUE, 
                                                           scientific = FALSE, big.mark = " "), "M")))) +
  geom_point_interactive(aes(
    x = Year, y = iMIGRANT_Stock_Females_World, color = "iMIGRANT_Stock_Females_World", group = "iMIGRANT_Stock_Females_World", 
    tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                    "<br>International MIGRANT Stock (Females):", 
                    paste0(format(x = iMIGRANT_Stock_Females_World, trim = TRUE,
                                  scientific = FALSE, big.mark = " "), "M")))) + 
  labs(title = "International MIGRANT Stocks in World (1990-2020)", y = "International MIGRANT Stock") +
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  scale_color_manual(
    values = c("iMIGRANT_Stock_Total_World" = "black", "iMIGRANT_Stock_Males_World" = "blue", "iMIGRANT_Stock_Females_World" = "pink"), 
    labels = c("iMIGRANT_Stock_Total_World" = "Both Sexes Combined", 
               "iMIGRANT_Stock_Males_World" = "Males",  "iMIGRANT_Stock_Females_World" = "Females")) +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "top", legend.title = element_blank())
girafe(ggobj = LINEs_INTERACTIVE_THREE)

Function geom_line_interactive() serves as an alternative to geom_line() to make it interactive. A download button is also available.

3.2.1.2 Stacked Barplots

#COLORs
CRs <- c("#339900", "#FF9900", "#003366", "#FF0000", "#663399", "#FFCC00", "#999999")

#N°1
BARs <- ggplot(iMIGRANT_Stock.FDATA, aes(x = Year, y = iMIGRANT_Stock_Total, fill = Birth, 
                                         #For N°3
                                         text = paste(" Residence:", Residence, "<br>", "Birth:", Birth, "<br>", 
                                                      "Year:", Year, "<br>", "International MIGRANT Stock:", 
                                                      format(x = iMIGRANT_Stock_Total, trim = TRUE, scientific = FALSE, big.mark = " ")))) +
  #STACKEd
  geom_bar(stat = "identity") + 
  labs(title = "International MIGRANT Stocks in World from 6 Areas (1990-2020)", 
       y = "International MIGRANT Stock - Both Sexes Combined") +
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  scale_fill_manual(values = CRs) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
BARs

We can see International Migrant Stock Global Evolution from 150M in 1990 to more than 250M in 2020. We also notice a more marked evolution in ASIA than in EUROPE.

Birth Areas are in A-Z order, we will have to use factor levels to reorder Birth Areas.

Non-positive comments are same as for Line Plots. OCEANIA and NORTHERN AMERICA are too small.

In Stacked Barplots above, we have data for a Residence-Birth pair and all Year in a row.

#Residence-Birth/Year To Year/Residence/Birth
iMIGRANT_Stock.FDATA_INTERACTIVE <- iMIGRANT_Stock.FDATA %>% group_by(Year, Residence, Birth) %>% 
  summarise(Share_of_iMIGRANT_Stock_Total = sum(Share_of_iMIGRANT_Stock_Total), iMIGRANT_Stock_Total = sum(iMIGRANT_Stock_Total))

#Table 1990/World
kable(iMIGRANT_Stock.FDATA_INTERACTIVE[1:7, c(1:3, 5)], row.names = FALSE, 
      align = c("l", "l", "l", "c"),
      caption = "International MIGRANT Stocks in World from 6 Areas in 1990")

International MIGRANT Stocks in World from 6 Areas in 1990
Year	Residence	Birth	iMIGRANT_Stock_Total
1990	WORLD	AFRICA	20621464
1990	WORLD	ASIA	56957000
1990	WORLD	EUROPE	47730278
1990	WORLD	LATIN AMERICA AND THE CARIBBEAN	15273399
1990	WORLD	NORTHERN AMERICA	2802135
1990	WORLD	OCEANIA	961547
1990	WORLD	OTHER	8640334

Now, we need to have data for a Year and all Residence-Birth pairs in a row.

#N°2
BARs_INTERACTIVE_One <- plot_ly(data = iMIGRANT_Stock.FDATA_INTERACTIVE, x = ~Year, y = ~iMIGRANT_Stock_Total, 
                                color = ~Birth, type = "bar", colors = CRs, legendgroup = 'Birth', 
                                text = with(iMIGRANT_Stock.FDATA_INTERACTIVE, paste(" Residence:", Residence, "<br>", 
                                                                                    "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                                                    "International MIGRANT Stock:", 
                                                                                    paste0(format(iMIGRANT_Stock_Total, trim = TRUE, 
                                                                                                  scientific = FALSE, big.mark = " ")))),
                                hoverinfo = "text") %>% 
  layout(barmode = "stack", 
         yaxis = list(title = "International MIGRANT Stock - Both Sexes Combined"),
         title = "International MIGRANT Stocks in  World from 6 Areas (1990-2020)", 
         legend = list(title = list(text='<b> Birth </b>'), x=1, y=0.5))
BARs_INTERACTIVE_One

Birth Areas are in Z-A order, we will have to use factor levels to reorder Birth Areas.

#N°3
BARs_INTERACTIVE_Two <- ggplotly(BARs) %>%
  layout(title = list(text = "International MIGRANT Stocks in World From 6 Areas (1990-2020)", x = 0.5))
BARs_INTERACTIVE_Two

#N°4
BARs_INTERACTIVE_THREE <- ggplot(iMIGRANT_Stock.FDATA) + 
  geom_bar_interactive(aes(x = Year, y = iMIGRANT_Stock_Total, fill = Birth, 
                           tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                           "<br>International MIGRANT Stock:", 
                                           paste0(format(iMIGRANT_Stock_Total, trim = TRUE, scientific = FALSE, big.mark = " ")))), 
                       position = "stack", stat = "identity") +
  labs(title = "International MIGRANT Stocks in World from 6 Areas (1990-2020)", 
       y = "International MIGRANT Stock - Both Sexes Combined") +
  scale_fill_manual(values = CRs) + 
  scale_y_continuous(labels = scales::label_number(scale = 1e-6, suffix = "M")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
girafe(ggobj = BARs_INTERACTIVE_THREE)

3.2.2 Shares

Stacked Barplots will show Shares in a Residence Area from \(n\) Birth Areas.

#N°2
BARs_INTERACTIVE_SHAREs_One <- plot_ly(data = iMIGRANT_Stock.FDATA_INTERACTIVE, x = ~Year, y = ~Share_of_iMIGRANT_Stock_Total, 
                                       color = ~Birth, type = "bar", colors = CRs, legendgroup = 'Birth', 
                                       #HOVER...
                                       text = with(iMIGRANT_Stock.FDATA_INTERACTIVE, paste(" Residence:", Residence, "<br>", 
                                                                                           "Birth:", Birth, "<br>", "Year:", Year, "<br>", 
                                                                                           "International MIGRANT Stock:",
                                                                                           round(Share_of_iMIGRANT_Stock_Total, 3), "%")), 
                                       hoverinfo = "text") %>% 
  layout(barmode = "stack", 
         yaxis = list(title = "International MIGRANT Stock - Both Sexes Combined (%)"),
         title = "International MIGRANT Stocks (%) in World from 6 Areas (1990-2020)", 
         legend = list(title = list(text='<b> Birth </b>'), x=1, y=0.5),
         margin = list(t = 30))
BARs_INTERACTIVE_SHAREs_One

Shares of International Migrant Stock are increasing in EUROPE (see section 3.2), decrease in LATIN AMERICA AND THE CARIBBEAN, look constant in AFRICA.

#N°4
BARs_INTERACTIVE_SHAREs_THREE <- ggplot(iMIGRANT_Stock.FDATA) +
  geom_bar_interactive(aes(x = Year, y = Share_of_iMIGRANT_Stock_Total, fill = Birth, 
                           #HOVER...
                           tooltip = paste("Residence:", Residence, "<br>Birth:", Birth, "<br>Year:", Year, 
                                           "<br>International MIGRANT Stock:", round(Share_of_iMIGRANT_Stock_Total, 3), "%")), 
                       stat = "identity") + 
  labs(title = "International MIGRANT Stocks (%) in World from 6 Areas (1990-2020)", 
       y = "International MIGRANT Stock - Both Sexes Combined (%)") +
  scale_fill_manual(values = CRs) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "right")
girafe(ggobj = BARs_INTERACTIVE_SHAREs_THREE)

3.2.3 Evolution and Shares

Combine Stocks and Shares thanks to subplot().

#N°2 (x2)
BARs_INTERACTIVE_One <- BARs_INTERACTIVE_One  %>% 
  layout(annotations = list(x = 0.5, y = 1.05, xref = "paper", yref = "paper", 
                            text = "International MIGRANT Stocks in World from 6 Areas (1990-2020)", showarrow = FALSE))
BARs_INTERACTIVE_SHAREs_One <- BARs_INTERACTIVE_SHAREs_One %>%
  layout(title = "",
         annotations = list(x = 0.5, y = 1.05, xref = "paper", yref = "paper", 
                            text = "International MIGRANT Stocks (%) in World from 6 Areas (1990-2020)", showarrow = FALSE))
BARs_INTERACTIVE_STOCKs_SHAREs_One <- subplot(BARs_INTERACTIVE_One, BARs_INTERACTIVE_SHAREs_One, 
                                              nrows = 2, heights = c(0.5, 0.5),
                                              titleX = TRUE, titleY = TRUE, shareX = TRUE) %>%
  layout(showlegend = TRUE)
BARs_INTERACTIVE_STOCKs_SHAREs_One

We can allow a shared x-axis. We have labels twice and have to force to have y-axis for Stocks and Shares.

Remarks:

Combine static Stocks and Shares with grid.arrange(). We can have one labels
For ggiraph, it’s not doable
Combination is less flexible in the application

3.2.4 Resume

We will use plotly or ggplot2 combine with ggiraph to make our Line Plots and Stacked Barplots.

#LibrariesCOMPARISON
LibrariesCOMPARISON <- data.frame(
  Library = c("ggplot2", "plotly", "ggplotly", "ggiraph"),
  Positive = c("Easy | Nice Graphics", 
               "Interactive | Download Button | Hover Text | Shared x-axis", 
               "Interactive | Download Button", 
               "Interactive | Download Button | Hover Text | Easy"), 
  Negative = c("Static", "Labels Issues", "Labels Issues | Hover Text", "-"))

#Table
datatable(LibrariesCOMPARISON, options = list(scrollX = TRUE, autoWidth = TRUE), 
          style = "bootstrap", rownames = FALSE, caption = "Graphics' Libraries Comparison")

3.3 Data Manipulations

Data Manipulations can take time. We need to do it to avoid loading times on client/server side.

3.3.1 Development Levels

There are 3 Development Levels from OHRLLS in International Migrant Stock Data (see section 3):

Least Developed Countries (LDC)
Land-Locked Developing Countries (LLDC)
Small Island Developing States (SIDS)

We have found method. data from UNSD which lists countries as LDC | LLDC | SIDS. There are 3 variables (LDC | LLDC | SIDS), with a small cross (x) if an area is in one level (an area can be in 2 levels at once). We create a new variable with 5 modalities:

LDC* | LLDC* | SIDS* | LDC/LLDC | LDC/SIDS

We have rows based on LDC | LLDC | SIDS in International Migrant Stock Data. We want to create new ones to retrieve data that follow our new variable (5 modalities):

LDC for LDC* and LDC+LLDC for LDC/LLDC \(\Rightarrow\) LDC will be included twice in International Migrant Stock Data

Vanuatu ceased to be a LDC in December 2020 (46 To 47) (Sources: 1 | 2)
Martinique, Turks and Caicos Islands, Guadeloupe, Cayman Islands and Bermuda (53 To 58) were not included in method. data from UNSD (Source)
Bahrain is also a SIDS since we need it to retrieve the correct data thanks to countries data (58 To 59)